by datastudy.nl

Head-to-head comparisons of frontier AI models, rebuilt daily from public benchmark data

Head to head

Claude Sonnet 5.5 vs Gemini 3.8 Flash

How Claude Sonnet 5.5 and Gemini 3.8 Flash stack up across benchmarks, pricing, speed, and the workloads that matter.

Claude Sonnet 5.5

Anthropic · Proprietary

Blended price$2.89per 1M tokens
Context1Mtokens
Speed42tok/s
Benchmarks47results
VS

Gemini 3.8 Flash

Google · Proprietary

Blended price$1.08per 1M tokens
Context1.05Mtokens
Speed86.7tok/s
Benchmarks13results

Claude Sonnet 5.5 costs $2.89 per million blended tokens, about 2.67x the price of Gemini 3.8 Flash. On reasoning, Claude Sonnet 5.5 leads by about 12 points in our normalized benchmark average.

The table below breaks down every workload axis where both models have published results. Each score is a normalized 0 to 1 average across the benchmarks tagged with that category. A gap of a few points is noise; a gap of ten or more is a real difference in capability. Where one model has not reported a result, the cell shows n/a and the missing benchmark does not drag its average down.

Benchmark scores by workload

WorkloadClaude Sonnet 5.5Gemini 3.8 FlashEdge
Reasoning 70% 58% +11.6 pts
Coding 70% 61% +9.4 pts
Agents & tool use 63% 55% +7.9 pts
Math 76% n/a n/a
Vision & multimodal 66% 67% +0.5 pts
Long context n/a 87% n/a
Writing 60% n/a n/a
Knowledge & factuality 71% 45% +26.1 pts

The headline benchmarks below are the most widely cited individual tests. GPQA Diamond measures graduate-level reasoning in physics, chemistry, and biology. SWE-Bench Verified tests whether a model can fix real GitHub issues. MMMU-Pro covers college-level multimodal understanding across six disciplines. AIME and FrontierMath push competitive and research math. BrowseComp measures web research ability.

Headline benchmarks

BenchmarkClaude Sonnet 5.5Gemini 3.8 Flash

Benchmarks tell you what a model can do in a controlled setting. They do not tell you whether it will work on your specific task, with your specific data, at your specific scale. The recommendations below map each common workload to whichever of these two models scores higher on the relevant axis. Use them as a starting point, not a final answer.

Which should you choose?

Hard reasoning

Multi-step analysis, research, and problems that need sustained thought.

Claude Sonnet 5.5

Coding & software

Writing, reviewing, and debugging code across a real codebase.

Claude Sonnet 5.5

Agents & tool use

Long-running agents that call tools, browse, and act on their own.

Claude Sonnet 5.5

Math & science

Formal math, competitive problems, and quantitative science.

Claude Sonnet 5.5

Vision & documents

Reading images, screenshots, charts, and dense documents.

Gemini 3.8 Flash

Long documents

Whole codebases, books, and transcripts that fill the context window.

Gemini 3.8 Flash

Writing & drafting

Copy, emails, and long-form drafting where tone matters.

Claude Sonnet 5.5

High-volume & cost-sensitive

Cheap, repetitive calls where the blended token price dominates.

Gemini 3.8 Flash

Low latency & interactive

Chat, autocomplete, and real-time experiences that need snappy responses.

Gemini 3.8 Flash

Scores are normalized from public benchmarks published by llm-stats.com and averaged per workload. Pricing is the blended input/output cost per million tokens at an 8:1 mix. Refreshes daily. How this works.